Augmented DT-CWT feature based classification using Regularized Neighborhood Projection Discriminant Analysis for face recognition

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摘要

This paper presents a novel face recognition method which integrates the Augmented Dual-Tree Complex Wavelet Transform (ADT-CWT) representation of face images and Regularized Neighborhood Projection Discriminant Analysis (RNPDA) method. ADT-CWT first derives desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. Different from DT-CWT, which does not consider the structural characteristics of the face images, our representation method not only considers the statistical property of the input features but also adopts an Eigenmask to emphasize those important facial feature points. The dimensionality of the derivation of ADT-CWT feature is further reduced by using RNPDA, which directly obtain a set of optimal eigenvectors with a simple regression framework and thus can overcome the small sample size problem of NPDA. Extensive experiments have been made to compare the recognition performance of the proposed method with some popular dimensionality reduction methods on the FERET database, the extended YALEB database and the CMU PIE database. The results verify the effectiveness of the proposed method.

论文关键词:Face recognition,Regularized Neighborhood Projection Discriminant Analysis (RNPDA),Fisher Linear Discriminant (FLD),Augmented Dual-Tree Complex Wavelet Transform (ADT-CWT)

论文评审过程:Received 21 March 2010, Revised 27 September 2010, Accepted 5 October 2010, Available online 12 October 2010.

论文官网地址:https://doi.org/10.1016/j.patcog.2010.10.005